Set membership (SM) H∞ identification is investigated aimed to estimate a low order approximating model and its identification error, without requiring the selection of a-priori basis for the model class. An α-optimal algorithm is determined using time domain data and supposing l∞ bounded measurement errors and exponentially stable systems. The presented algorithm is proven to be strongly convergent.
α-Optimality evaluation in H∞ identification of low-order uncertainty models / Giarrè, L.; Malan, S.; Milanese, M.. - 1:(1997), pp. 175-176. (Intervento presentato al convegno Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) tenutosi a San Diego, CA, USA, nel 1997).
α-Optimality evaluation in H∞ identification of low-order uncertainty models
Giarrè L.;
1997
Abstract
Set membership (SM) H∞ identification is investigated aimed to estimate a low order approximating model and its identification error, without requiring the selection of a-priori basis for the model class. An α-optimal algorithm is determined using time domain data and supposing l∞ bounded measurement errors and exponentially stable systems. The presented algorithm is proven to be strongly convergent.
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